Batters



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Showing page 438 of 4181 (83616 total matches)
YEAR TEAM
ID 		NAME PLATE
APP 		ON
BASE 		AT
BATS 		TOTAL
BASES 		OB
AVG 		SLG
AVG 		OPS OPP
PIT
OB 		OPP
PIT
SLG 		OPP
PIT
OOPS 		EXPCT
OB
AVG 		EXPCT
SLG
AVG 		EXPCT
OPS 		LG
OPS 		PME
OB 		PME
SLG 		PME PF PME
OB
PF 		PME
SLG
PF 		PME
PF
1965 KC1 Jim Gentile 128 39 118 64 0.305 0.542 0.847 0.319 0.380 0.699 0.312 0.461 0.773 0.676 -2.3 16.2 13.9 1.01 -2.3 16.1 13.8
1984 KCA Darryl Motley 557 177 522 230 0.318 0.441 0.758 0.315 0.392 0.707 0.316 0.416 0.733 0.722 0.8 12.8 13.6 0.98 0.8 13.0 13.8
2008 LAN Russell Martin 650 250 553 219 0.385 0.396 0.781 0.324 0.417 0.741 0.354 0.407 0.761 0.740 19.8 -6.3 13.5 0.97 20.0 -6.2 13.8
2021 MIL Luis Urias 570 195 490 218 0.342 0.445 0.787 0.316 0.418 0.734 0.329 0.432 0.761 0.723 7.5 6.4 13.8 0.98 7.5 6.4 13.8
1962 MIN Earl Battey 591 203 522 204 0.343 0.391 0.734 0.306 0.378 0.684 0.325 0.384 0.709 0.716 11.3 2.7 13.9 1.00 11.2 2.7 13.8
2020 MIN Eddie Rosario 231 73 210 100 0.316 0.476 0.792 0.311 0.357 0.668 0.313 0.417 0.730 0.732 0.6 13.3 13.9 1.01 0.6 13.2 13.8
2016 MIN Robbie Grossman 389 150 332 147 0.386 0.443 0.828 0.327 0.432 0.759 0.356 0.438 0.794 0.743 11.4 1.8 13.2 1.01 11.9 1.9 13.8
1950 NY1 Al Dark 643 209 587 258 0.325 0.440 0.765 0.326 0.395 0.721 0.325 0.417 0.743 0.733 -0.3 13.1 12.7 0.99 -0.3 14.2 13.8
1977 NYA Chris Chambliss 653 219 600 267 0.335 0.445 0.780 0.333 0.398 0.731 0.334 0.422 0.756 0.732 0.7 14.2 14.9 1.02 0.6 13.2 13.8
2012 NYA Mark Teixeira 524 174 451 214 0.332 0.475 0.807 0.329 0.418 0.747 0.330 0.446 0.777 0.729 0.9 13.2 14.2 1.00 0.9 12.8 13.8
1941 PHA Dick Siebert 513 195 467 215 0.380 0.460 0.841 0.365 0.415 0.780 0.373 0.437 0.810 0.726 3.8 10.5 14.4 1.02 3.6 10.1 13.8
1924 PHI Butch Henline 329 117 289 123 0.356 0.426 0.781 0.313 0.371 0.684 0.334 0.398 0.732 0.721 7.1 8.0 15.1 1.08 6.5 7.3 13.8
1985 PHI Von Hayes 637 211 570 227 0.331 0.398 0.729 0.318 0.363 0.681 0.325 0.381 0.705 0.689 4.1 10.1 14.2 1.03 4.0 9.8 13.8
1960 PIT Rocky Nelson 234 89 200 94 0.380 0.470 0.850 0.333 0.399 0.732 0.357 0.435 0.791 0.704 5.6 7.5 13.1 0.97 5.9 7.9 13.8
2016 SDN B. J. Upton 374 113 344 151 0.302 0.439 0.741 0.305 0.403 0.708 0.303 0.421 0.725 0.732 2.0 12.2 14.2 1.05 1.9 11.9 13.8
1980 SDN Luis Salazar 183 67 169 78 0.366 0.462 0.828 0.304 0.371 0.676 0.335 0.416 0.752 0.691 5.6 7.5 13.2 0.96 5.9 7.8 13.8
2013 SEA Raul Ibanez 496 152 454 221 0.306 0.487 0.793 0.322 0.407 0.729 0.314 0.447 0.761 0.723 -3.8 17.7 13.8 0.98 -3.8 17.7 13.8
1944 SLA Al Zarilla 330 122 287 129 0.370 0.449 0.819 0.337 0.376 0.713 0.354 0.413 0.766 0.672 5.4 11.2 16.5 1.10 4.5 9.4 13.8
1919 SLA Ken Williams 266 96 227 106 0.361 0.467 0.828 0.339 0.381 0.720 0.350 0.424 0.774 0.681 3.0 9.5 12.5 1.03 3.3 10.5 13.8
1984 SLN George Hendrick 480 155 441 179 0.323 0.406 0.729 0.305 0.363 0.668 0.314 0.385 0.698 0.685 4.4 9.4 13.8 1.01 4.4 9.4 13.8
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Columns:
--------

Note: The batter's composite OB% and SLG% is obtained by the sum of all individual
plate appearances. For each PA, the OB% and SLG% used is versus pitchers of the same
hand as the one he's facing.

OPP_PIT_OB: the opposing pitcher OB% against, when facing batters of the same hand
OPP_PIT_SLG: the opposing pitcher SLG% against, when facing batters of the same hand
OPP_PIT_OOPS: the opposing pitcher OB% + SLG% against, when facing batters of the same hand

EXPCT_OB_AVG: the average of the opposing pitcher's OPP_PIT_OB and the batter's OB% (vs. L or R)
EXPCT_SLG_AVG: the average of the opposing pitcher's OPP_PIT_SLG and the batter's SLG% (vs. L or R)
EXPCT_OPS: the average of the opposing pitcher's OOPS and the batter's OPS (vs. L or R)

LG_OPS: the average league OPS, with the league of the home park being the league

PME_OB: the cumulative result of the plate appearance minus the EXPCT_OB_AVG
PME_SLG: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG
PME: the cumulative result of the plate appearance minus the EXPCT_OPS

PF: the composite park factor the batter experienced, based on lefty-righty and park

PME_OB_PF: the cumulative result of the plate appearance minus the EXPCT_OB_AVG, with PF
PME_SLG_PF: the cumulative result of the plate appearance minus the EXPCT_SLG_AVG, with PF
PME_PF: the cumulative result of the plate appearance minus the EXPCT_OPS, with PF


On every pitcher versus batter matchup, we have a contest of the batter's ability and
the pitcher's ability. Although OPS and OOPS are not perfect statistics, they are
widely embraced and are relatively straightforward for most fans. They're approximations.
At some point, this process can be made smarter. Until then, this is where we are.

What is the batter's average ability on any plate appearance in a season? It's his OPS for the
season. Likewise, the pitcher's OOPS on the play is his seasonal OOPS. What is the expected
outcome? It's the average of the two, of course.

However, we have two issues to deal with -- the handedness (L or R) of the batter and pitcher
and the park where each event occurred.

1) Hand: For each and every PA, the expected outcome is affected by the hand of the batter and
pitcher. But, we only care about the batter's and pitcher's seasonal OPS/OOPS when it matches
the same scenario as the specific PA.

For example: If a left-handed batter is facing a right-handed pitcher, we only care about how
the batter did versus right-handed pitchers that year, and how the pitcher did versus left-handed
batters. Those are the specific OPS/OOPS values used from which to build the expected outcome.

Ex.: A LHB faces a RHP. The batter's OPS versus righties that year was 0.800. The pitcher's OOPS
versus lefties was 0.700. The expected outcome is the average of the two, 0.750.

Suppose the batter makes an out. His on-base average on the play was 0.000 and his slugging average
is also 0.000. On the play, the batter attained a negative PME, 0.000 minus 0.750 = -0.750. Meanwhile,
the pitcher attained a positive PME of 0.750 minus 0.000 = 0.750. All plays balance in this way.

What if the batter singles? His OB% was 1.000 and his SLG% is 1.000. That's an OPS of 2.000. His PME
is 2.000 minus 0.750 = 1.250, and the pitcher's PME is 0.750 minus 2.000 = -1.250.

All ~16 million plays in MLB from 1910-2025 were assessed in this manner.



2) Park: The parks where events occurred are important as well. Using the enhanced Park Factors at
this site -- those which break down PFs by L-L, L-R, R-L, R-R by using a base counting method -- a
composite PF is derived based on all of the PAs a batter had that season. After the seasonal PME is
compiled by adding all of the plays that year, the PME is divided by the PF* to obtain the final PME.

* The PME is compiled at the home and road level and divided by the corresponding PF. The PFs may
not seem correct but are indicative of the season. For example, the Rockies of 2001 had a composite
PF of 1.22. Todd Helton's (as a lefty) was more like 1.18. On the road, he was 0.97 -- for a
composite of 1.08 (1.18 + 0.97) / 2, the value shown. Before applying the PF, his home PME was about
96 and road was 9. Thus, most of the PME reduction was caused at home. It drops by ~16% (twice 1.08)
while his road PME stays relatively constant. His park-adjusted PME drops from ~105 to 91.


NOTE: This analysis concerns only what the batter does at the plate. Things like base running and
the quality of the opposing defense is not factored in (aside from taking extra bases on a hit).